Radio frequency (RF) transmitters typically include a power amplifier (PA). The power amplifier may be operated in its non-linear region near saturation in order to increase the power efficiency. Due to the non-linearity of the power amplifier, the adjacent channel leakage ratio (ACLR) level becomes unacceptable, because the output spectrum expands and causes interference with adjacent transmission channels. To fix this problem, an amplifier linearization technique is performed by employing an adaptive digital pre-distorter (DPD). The digital pre-distorter linearizes the power amplifier by generating a nonlinear transfer function that is the inverse to the power amplifier in such a way, that when the digital pre-distorter precedes the power amplifier, the overall system—digital pre-distorter plus the power amplifier—is close to being linear.
To compute and constantly update the parameters of the digital pre-distorter, a digital signal processor (DSP) may capture two signals: i) the transmitted (reference) signal and ii) the power amplifier output (i.e., feedback). A digital pre-distorter typically comprises a combination of blocks configured in series: memory-less nonlinearity, linear and non-linear filters. The digital pre-distorter is usually implemented in an application-specific integrated circuit (ASIC) or field programmable gate array (FPGA).
Most available adaptive predistorters for power amplifiers with memory effects are based on an indirect learning method, such as the learning method disclosed in C. Eun and E. Powers, “A New Volterra Predistorter Based On The Indirect Learning Architecture,” IEEE Transactions On Signal Processing”, Vol. 45, No. 1, January 1997, which is hereby incorporated by reference into the present disclosure as if fully set forth herein. FIG. 2 illustrates an exemplary configuration for implementing an indirect learning method. The method is called the indirect learning method because the post-inverse filter coefficients are first identified and are then copied to work as a predistorter. This is very popular since the computational complexity is lower than other methods.
Two drawbacks may affect the performance of the indirect learning model. First, the measurement of the output of a power amplifier may be noisy. Thus, the adaptive algorithm converges to biased values, as discussed in D. Zhou and V. DeBrunner, “Novel Adaptive Nonlinear Predistorters Based On The Direct Learning Algorithm,” IEEE Transactions On Signal Processing”, Vol. 55, No. 1, January 2007, which is hereby incorporated by reference into the present disclosure as if fully set forth herein.
Second, the nonlinear filters cannot be commuted. That is, the identified adaptive inverse model is actually a post-inverse model. Thus, placing a copy of this model in front of the nonlinear device does not guarantee a good pre-inverse model for the nonlinear device. These drawbacks are not in the direct learning architecture.
Therefore, there is a need in the art for an improved predistortion method and apparatus for use with a power amplifier of a transmitter.